Real-time needle shape prediction in soft-tissue based on image segmentation and particle filtering
Bibliographic record
Abstract
Prostate brachytherapy is a current technique used to treat cancerous tissue in the prostate by permanently implanting radioactive seeds through the use of long flexible needles. This paper proposes a real-time method to predict the shape of a flexible needle inserted into soft tissue using axial Transrectal Ultrasound (TRUS) image segmentation and a non-holonomic bicycle model informed via particle filter. The needle location is tracked in TRUS images to capture the needle shape up to a specified depth. Through the use of a particle filter the noisy tracked needle shape is used to update the parameters of a kinematic bicycle model in a robust manner to predict the shape of the entire needle after it is fully inserted. The method is verified in both ex-vivo beef phantom tissue and in-vivo clinical images, yielding an average tip prediction error of less that 0.5 mm in both the ex-vivo and in-vivo image sets with a peak processing time of less than 9.5 ms per image frame.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".